301 results about "Decision strategy" patented technology

The invention relates to the technical field of artificial intelligence and enterprise decision making, and discloses an intelligent decision making system and method based on an enterprise life index large model, and the system comprises a multi-source heterogeneous data collection module which is used for obtaining and associating various data in real time, and generating target enterprise multi-source heterogeneous data; the dynamic index system generation module is used for sequencing feature importance through a graph neural network and generating a dynamic evaluation index set containing survival, risk and growth indexes; the enterprise life index large model building module is used for fusing index sets, capturing enterprise state evolution, embedding risk factors and outputting multi-dimensional enterprise life indexes; the intelligent decision dynamic generation module is used for inputting the indexes into the decision strategy tree model and matching a strategy library to generate a decision instruction set; and the closed-loop feedback optimization module is used for realizing closed-loop optimization by acquiring execution data and combining Bayesian optimization dynamic correction parameters. According to the invention, the accuracy and timeliness of enterprise intelligent decision making can be improved.

The invention relates to the technical field of information retrieval, and discloses a multi-modal fusion and reinforcement learning collaborative retrieval enhancement generation method and system. The method comprises the following steps: receiving an original query input by a user, and generating a sub-query based on a large language model in combination with a multi-modal context of a current iteration step; forming a current state in combination with the sub-query and the multi-modal context, modeling a retrieval enhancement generation task as a Markov decision process, and adaptively selecting an optimal action from a predefined action set in the current state by utilizing a large language model according to a decision strategy; executing a corresponding multi-modal retrieval operation according to the optimal action, fusing the obtained multi-modal information, generating an intermediate answer or a final answer of the sub-query, and updating a multi-modal context by using the intermediate answer; off-line training optimization is carried out on the large language model through imitation learning and a calibration chain, and decision strategies and sub-queries are inferred online through the model after fine adjustment. According to the invention, more efficient and accurate complex query processing is realized.

The invention relates to the technical field of AI interaction data processing, and discloses an AI interaction data processing system based on multi-modal perception and dynamic decision, comprising the following modules: a multi-modal perception module used for collecting environment data through a multi-source sensor; the data fusion module is used for generating fused feature data; the causal decision-making module is used for generating a decision-making action to cope with the change of the environment; the decision security module is used for identifying potential safety hazards in the high-risk scene and generating alternative decision or early warning information; the sensing calibration module is used for optimizing a sensing strategy in a changing environment; and the adaptive optimization module continuously optimizes the perception and decision strategy. According to the invention, the multi-modal sensing module is combined with a cross-modal consistency learning mechanism and a noise robustness enhancement technology, and the data fusion module introduces a context sensing attention mechanism and a multi-level feature alignment network, so that the sensing ability of the system to complex environment information and the comprehensiveness and accuracy of feature representation are effectively improved.

The invention provides a military clothing production scheduling optimization method and system based on an intelligent algorithm, and the method achieves the intelligent management of a production process through the construction of a dynamic state model and a disturbance cost evaluation system. Firstly, a reference production plan is generated by using a global optimization algorithm, and an event sensing module is deployed to monitor the production process in real time. When a task insertion event is captured, the system automatically collects related production parameters, triggers a disturbance cost evaluation mechanism, and dynamically predicts and quantifies the comprehensive influence of different interruption schemes. And based on the quantification result and a preset decision strategy, the system determines an optimal interrupt execution scheme, calls a rapid local rearrangement algorithm coupled with a disturbance cost index, adaptively adjusts the affected plan segments, and finally generates and issues an optimized production instruction sequence. According to the method, the whole process from disturbance identification to scheme execution is intelligentized, and the problem of dynamic scheduling for emergency tasks in military clothing production is effectively solved.

The present invention discloses a method and system for smart grid fault self-healing based on generative artificial intelligence, which relates to the field of smart grid fault self-healing. The method includes: obtaining and preprocessing multi-source heterogeneous data; generating a pre-trained language model and performing domain adaptability fine-tuning, adding a task-specific output layer; performing feature fusion using a multimodal data fusion method, and using annotated historical fault data sets for supervised training to obtain fault diagnosis results. Fault self-healing is modeled as a Markov decision process, and a deep reinforcement learning algorithm is used to interactively learn the optimal decision-making strategy with a power grid simulation environment, and an optimal fault self-healing plan is generated and converted into specific operation instructions. The present invention integrates multi-source heterogeneous data, performs accurate positioning and description generation, improves the reliability and self-healing ability of the smart grid, and ensures rapid recovery of normal operation when a power grid fails.

The invention discloses an automated guided vehicle scheduling optimization method and device, a medium and a terminal, and the method comprises the steps: generating fleet heterogeneous data based on parameter data of a plurality of automated guided vehicles in an intelligent warehouse, and fusing the fleet heterogeneous data into an undirected graph model obtained through undirected graph modeling based on storage environment topological data in advance, the method comprises the steps of obtaining an environment topological graph model, then carrying out graph topology and heterogeneous feature extraction on the environment topological graph model to obtain a multi-constraint path planning model, and dynamically adjusting a single-vehicle route of each automated guided vehicle by utilizing the planning model and real-time storage field state data. And then performing scheduling process simulation on the intelligent warehouse based on the dynamic routing table formed by adjustment and historical scheduling index data of the intelligent warehouse, so that a scheduling system model learns a mapping strategy from a state to a behavior and outputs an optimization decision strategy. The method is used for automatic guide vehicle scheduling, is suitable for storage environments of different scales and complexities, and improves the scheduling efficiency and accuracy.

The invention discloses an automatic driving vehicle highway intelligent lane changing method based on reinforcement learning, which optimizes a highway automatic driving decision strategy by designing an improved Huber regularization reward threshold adaptive double depth Q network (HRA-DDQN) framework. The method comprises the following steps: firstly, constructing a state space and an action space to describe dynamic behaviors of a vehicle, and introducing a comprehensive reward function based on safety, efficiency and lane changing requirements; secondly, through a dynamic target network updating mechanism triggered by reward differences, the adaptability of the model to traffic environment changes is improved; and finally, introducing a hybrid loss function combining Huber loss and L2 regularization to enhance the robustness and generalization ability of the model. Simulation results show that the driving efficiency and safety of the automatic driving vehicle in the expressway scene are remarkably improved, the collision rate is reduced, the stability of lane changing decision making is optimized, and the method is suitable for the complex and changeable expressway traffic environment.

The invention discloses an intelligent logistics system based on optimal scheduling, and relates to the field of logistics management, and the system comprises the steps: arranging environment sensors at key nodes of a logistics network, and transmitting collected real-time data to edge computing equipment for preprocessing; analyzing the preprocessed data by using a deep learning model, and predicting an environment change trend in a non-period of time; dynamically adjusting a logistics scheduling strategy based on the prediction result; after receiving the adjusted scheduling strategy, defining a state space, an action space and a reward function of the logistics system; training a reinforcement learning algorithm model, and continuously optimizing a decision strategy through interaction with the environment; and calling the reinforcement learning model through an API interface to make a real-time decision, and generating an optimal scheduling scheme. According to the invention, by integrating environment perception, deep learning prediction, adaptive scheduling and reinforcement learning, the defects of an existing intelligent logistics system in the aspects of environment perception, prediction and dynamic scheduling optimization are overcome, and the intelligent level and operation efficiency of logistics management are significantly improved.

The invention belongs to the technical field of unmanned aerial vehicle clusters, and particularly relates to an unmanned aerial vehicle cluster hierarchical behavior decision-making method oriented to ground hunting, which comprises the following steps of: 1, determining various constraint conditions and income targets in a ground hunting task so as to establish a random game model oriented to the ground hunting task; the second step of considering the prediction problem of the motion trail of the surrounding target and the optimization problem of the multi-agent decision algorithm, and establishing an unmanned aerial vehicle cluster behavior decision network based on the improved MASAC algorithm, and the third step of online decision making. According to the method, the random game model oriented to the ground surrounding task can be established, then the position information of the surrounding target at the next moment can be predicted, the predicted trajectory data is input into the bottom layer MASAC decision network, and finally the optimal unmanned aerial vehicle cluster behavior decision strategy oriented to the ground surrounding task is obtained through offline training. And the ground surrounding of the target is completed by adopting an offline trained unmanned aerial vehicle cluster behavior decision-making method.

The invention provides a vehicle lane changing planning method and system based on a graph neural network and multiple agents, and relates to the technical field of unmanned driving, and the method comprises the steps: obtaining a traffic scene graph structure with multiple agents; using a multi-agent reinforcement learning algorithm and a corresponding reward function to train the graph neural network fusion model, and updating parameters of the graph neural network fusion model by minimizing dominant function-based strategy gradient loss and value function loss to obtain a trained graph neural network fusion model; analyzing the traffic scene graph structure by using the trained graph neural network fusion model to obtain a multi-agent decision strategy; and performing decision-making prior fusion on the multi-agent decision-making strategy, performing dynamic interaction with the environment, obtaining a vehicle lane changing planning result, and completing the vehicle lane changing planning. According to the method, the problems of low multi-agent trajectory tracking precision and poor robustness in a complex scene are solved.

The invention discloses a multi-mode driven cross-industry digital twinning universal platform architecture and an implementation method, and relates to the technical field of digital twinning and artificial intelligence. The method comprises the following steps: establishing a multi-modal driven cross-industry digital twinning universal platform, deploying a multi-source heterogeneous data acquisition component in a data access layer to access text, image and time series data, and converting unstructured data into a unified feature space by adopting a Transform-GNN cross-modal encoder in a multi-modal fusion layer; in the large model scheduling layer, feature vectors are analyzed through a multi-modal large model center based on an industry knowledge graph, an algorithm is dynamically matched, and an initial decision strategy is generated; the method comprises the following steps of: establishing a parameterized template library, and supporting security cooperative training of a third-party algorithm scheduling engine on a third-party algorithm, and deploying a lightweight digital twin engine in a twin engine layer: establishing the parameterized template library: pre-defining three templates of geographic space, equipment assets and business processes; deploying the twin model to an edge node by adopting a knowledge distillation method; in the interactive application layer, loading the BIM / GIS model in a lightweight manner through a low-code tool, and completing scene construction through a dragging component; and rendering a twin state in real time through a three-dimensional cockpit, analyzing a natural language instruction and performing corresponding operation.

The invention discloses a personalized computer-aided decision-making method and system fusing multi-modal data, and relates to the field of personalized computer-aided decision-making, and the method comprises the steps: mapping multi-source heterogeneous modal data to a unified semantic embedding space, and obtaining a multi-modal unified representation vector set; carrying out three-layer progressive fusion on a feature layer, a situation layer and a decision layer of the multi-modal data to generate a global decision context vector; based on a cross attention mechanism, outputting a fused context sensing personalized vector; based on the behavior cloning model, outputting probability distribution on all decision options; according to the user feedback operation data, generating a user personalized decision strategy and performing dynamic optimization; and generating a structured decision report containing visual traceability information based on the hierarchical fusion process and decision reasoning logic. End-to-end intelligent generation from multi-source heterogeneous data to personalized decisions is realized, and a standardized process is converted into personalized customized decisions.

The invention discloses a deep learning-based cold resistance evaluation system for medicago sativa L., and the system comprises a data generation module which is used for generating a phenotypic image and corresponding physiological data of medicago sativa L. under low-temperature stress through a diffusion model embedded with plant low-temperature response physical constraints; the evaluation model module is used for extracting cold resistance characteristics from the image and physiological data by adopting a causal-driven dual-channel adaptive network; and the decision module comprises a hierarchical model distillation unit and a federal reinforcement learning unit, and the hierarchical model distillation unit and the federal reinforcement learning unit realize joint training of model compression and decision strategy optimization through an edge-cloud collaborative architecture, output a cold-resistant decision and realize visualization through an augmented reality interface. The method can effectively solve the core problems of traditional medicago sativa cold resistance assessment in the aspects of data generation, model generalization, decision-making efficiency and the like.

The invention discloses a multi-agent training method and system fusing a knowledge graph. The system comprises a knowledge graph management module, an agent training module and an interaction and feedback module. And the knowledge graph management module is responsible for organizing, updating and the like of the knowledge graph and covers data acquisition, cleaning, storage, construction, updating, query, reasoning and feedback collection. The agent training module comprises an environment simulator, a plurality of learning modules, a knowledge graph interaction module, a strategy generation module and a feedback mechanism, and helps an agent to learn and optimize a decision strategy in a simulation environment. The interaction and feedback module is provided with an interaction interface, a feedback calculation module, a knowledge graph association module and a performance monitoring and visualization module, and is used for realizing communication and performance evaluation between the intelligent agent and the environment; and the interaction and feedback module realizes environment interaction, feedback calculation, knowledge graph association and performance monitoring evaluation and adjustment.

The invention provides a high-speed train timetable optimization and rescheduling method based on deep reinforcement learning, relates to the technical field of high-speed train scheduling and resource optimization, and aims to realize efficient and accurate high-speed train timetable optimization and rescheduling. Comprising the steps that a strategy network is constructed based on a Markov decision process model of high-speed rail scheduling, and the strategy network learns an action decision strategy of the Markov decision process model of the high-speed rail scheduling; inputting the current station state into the strategy network to obtain an action decision as a departure sequence of all trains at the current station; based on a train state transition model, simulating a train operation environment according to a departure sequence of all trains in the current station, and determining arrival time and departure time of all trains in the current station, which meet traffic safety constraints; according to the above steps, the arrival time and departure time of all trains in each station on the running route are iteratively calculated in sequence, and a train timetable is obtained.

The invention provides an intelligent e-commerce behavior event decision-making method and system fusing multi-source perception, and the method comprises the steps: obtaining a user behavior data set, a commodity attribute data set and an environment context data set through a preset data collection interface, and carrying out the intention recognition processing of multi-source data; generating an intention feature set containing preliminary intention features and refined intention features, then generating an interactive guidance strategy tree containing node branch weights and strategy execution priorities based on the intention feature set, and performing dynamic path adjustment processing on the interactive guidance strategy tree according to a real-time feedback data set to obtain an optimized strategy tree set; and finally, pushing the optimization strategy tree set to a target interaction interface to activate an interaction guide operation, thereby providing personalized and intelligent interaction guide for the user by fusing multi-source sensing data, deeply understanding the intention of the user and dynamically adjusting a decision strategy, and improving the operation efficiency of an e-commerce platform and the satisfaction of the user.

The invention discloses an execution file processing method and system of a decision engine, and relates to the technical field of information. Creating a global execution context according to the parameter set; establishing a parameter hierarchical pool structure according to the global execution context; loading the entity object data to the parameter hierarchical pool structure according to the access authority configured by the association rule; obtaining an association rule corresponding to the decision set number from a preset rule base according to the decision set number; sorting the association rules according to the priorities and historical execution success rates of the association rules; sequentially executing the sorted association rules through a rule execution engine by utilizing a parameter hierarchical pool structure to obtain an association rule execution result set; selecting a predefined decision strategy; and taking the association rule execution result set as input, and calculating a decision result according to the selected decision strategy. Aiming at low rule execution efficiency in the prior art, the execution efficiency is improved.

The embodiment of the invention provides a decision strategy determination method and device based on an operational research decision large model, electronic equipment and a storage medium. The method comprises the steps that decision demand information is acquired; according to a pre-trained operational research decision-making large model, determining a decision-making strategy, the pre-trained operational research decision-making large model at least comprising a decision-making routing model and a decision-making sub-model connected with the decision-making routing model, and the decision-making routing model being used for determining the decision-making sub-model corresponding to the decision-making problem type; the decision sub-model is used for determining a decision strategy corresponding to the decision demand information, the operational research decision large model has higher accuracy, and the pre-trained large language model can learn language laws and knowledge from a large amount of data; therefore, higher accuracy and robustness are shown in information extraction tasks of constraint conditions, targets and the like related to domain problems, and the large language model has better adaptability to complex and unknown input and various different scenes.

The invention relates to the technical field of big data analysis and disaster management, and discloses a multi-dimensional spatio-temporal data analysis and visualization method, system and device. The method comprises the steps of obtaining multi-source spatio-temporal data and performing standardization processing to obtain processed spatio-temporal data; performing real-time analysis and trend prediction according to the processed spatio-temporal data based on a deep learning algorithm to obtain an analysis prediction result; performing three-dimensional modeling through a GIS platform according to the processed spatio-temporal data and an analysis prediction result to obtain a three-dimensional dynamic simulation model and performing visual display; and generating a decision strategy according to an analysis prediction result, and optimizing the decision strategy based on a reinforcement learning mechanism. According to the method, multi-source spatio-temporal data are efficiently integrated, real-time processing of large-scale data is achieved, and the data response speed is remarkably increased. Prediction is carried out based on multi-source data fusion and a deep learning algorithm, and the automation and accuracy of analysis are improved. Real-time dynamic display helps a user to understand a data trend more visually, and decision scientificity and timeliness are enhanced.

The invention relates to a multi-competency intelligent scoring method and system, and belongs to the technical field of intelligent evaluation and talent evaluation. The method comprises the following steps: acquiring multi-source response data of a target evaluation object, executing evidence sufficiency and consistency analysis for each competency dimension, generating a judgment state identifier, and constructing a structural description; performing responsibility distribution on the evaluation evidence, mapping the evaluation evidence into a corresponding evidence model and generating a priority constraint relationship; performing time sequence consistency and stability analysis on the evidence model, and constructing a time sequence constraint rule to prohibit unconstrained score backtracking; and carrying out resolution processing on the continuous undetermined dimension, generating a scoring result through a preset risk scoring rule and a conservative determination strategy, and synchronously generating a scoring basis path and confidence information. According to the method, standard management and control of multi-source evidences and accurate constraint of the whole scoring process are realized, the scoring accuracy and traceability are effectively improved, and reliable support is provided for multi-scene talent evaluation decision.

The invention discloses a multi-user-oriented smart home resource conflict negotiation and allocation method, which comprises the following steps of: detecting equipment use conflicts caused by at least two users by constructing a structural causal model for representing a causal relationship of a plurality of variables in a smart home environment, generating a candidate decision strategy set comprising resource isolation and alternative compensation, and allocating the candidate decision strategy set to the smart home environment; carrying out anti-fact inference by utilizing a causal model, quantifying the causal effect of each strategy on the user state, and selecting an optimal strategy for execution based on a minimum negative effect and a Pareto optimal principle; besides, the method ensures that the system dynamically adapts to user habit changes through online monitoring of prediction errors and correction of the causal model, and compared with the prior art, the method improves the decision accuracy through causal reasoning, realizes fair and personalized user resource allocation, and improves the user experience and long-term effectiveness of the smart home system.

The invention relates to the field of artificial intelligence cloud computing, in particular to a cloud native application intention driven intelligent arrangement system based on a large language model. The intention analysis module is used for receiving input data and carrying out feature extraction and vectorization decomposition; outputting an intention vector representation; the strategy planning module is used for receiving the intention vector representation, generating a decision strategy through a multi-objective optimization algorithm and outputting an arrangement strategy; the arrangement execution module is used for receiving the arrangement strategy, analyzing the arrangement strategy through a feature mapping network, converting the arrangement strategy into an instruction set and outputting execution result data; the model training module is used for receiving execution result data and input data and generating a weight updating event when parameters change; and the autonomous learning module is used for receiving the weight updating event, carrying out neural network processing, generating an improved arrangement strategy and forming an adaptive learning mechanism. According to the method, the natural language intention is analyzed through the large language model, and the accuracy and robustness of large-scale arrangement are improved in combination with the data-driven decision.

The embodiment of the invention provides a data classification and grading processing method and device, a medium and a program product, and relates to the technical field of data classification. The method comprises the steps of collecting multi-source data corresponding to tasks to be classified and graded, and extracting multi-modal features of the multi-source data by using a pre-trained multi-modal feature extraction model; processing the multi-modal features by using a pre-trained classification and grading model to obtain a data classification and grading result output by the classification and grading model; wherein the classification and grading model is formed by fusing a pre-trained multi-modal large model and a pre-trained vertical domain small model; and determining a final classification and grading decision strategy based on the classification and grading result. According to the embodiment of the invention, the data classification and grading task in the specific field is carried out in a mode of combining the large model and the small model, and the universality and the field specificity of data processing are effectively considered, so that the data classification and grading accuracy can be improved while the data privacy is protected.

The invention discloses a burying point analysis system for customer behavior analysis, and relates to the field of customer behavior analysis, the system comprises the following components: a data acquisition module, a digital twin model construction module, a business scene simulation module and a behavior prediction and decision support module; according to the invention, the digital twinborn model which accurately reflects customer behavior characteristics is constructed by using the digital twinborn model construction module and combining a deep learning algorithm and a data mining technology, and the model is imported into a diversified virtual business scene for behavior simulation through the business scene simulation module, so that the customer behavior simulation efficiency is improved. And finally, the behavior prediction and decision support module accurately predicts customer behaviors and generates a personalized decision plan by applying a space-time correlation prediction algorithm and an intelligent decision recommendation algorithm based on a knowledge graph, so that the customer behaviors can be accurately predicted. The functions jointly enhance the insight ability of enterprises for customer behaviors.

The invention discloses a low-speed unmanned vehicle artificial intelligence decision and performance evaluation system, and belongs to the technical field of low-speed unmanned vehicle scheduling, and the system comprises a generation module which is used for generating a scene novelty index through monitoring the instantaneous offset of an environment parameter; the topology module is used for constructing a dynamic scene topology according to the scene novelty index, and the dynamic scene topology comprises nodes and edge weights generated based on the scene novelty index; the decision-making module is used for converting the edge weight into a decision-making rule according to the dynamic scene topology and generating a decision-making strategy; the performance calculation module is used for tracking the deviation between the vehicle motion parameter and the control instruction during the execution of the decision strategy to calculate a performance disturbance coefficient; and a topology calibration module. The method can effectively adapt to unrecorded edge scenes such as sudden congestion and severe weather superposition, improves the generalization ability of a system to complex scenes exceeding a pre-training range, and reduces decision errors caused by insufficient scene adaptation.

The invention discloses a hybrid pumped storage scheduling method for reducing the operation water consumption rate of cascade hydropower stations, which comprises the following steps: establishing a cascade water-light-storage complementary power generation scheduling model, and constructing a target function with the minimum total water consumption of the cascade hydropower stations based on the cascade hydropower scheduling model; determining an objective function constraint condition according to the cascade hydropower dispatching model; establishing a solving model on the basis of an Actor-Critic algorithm; time sequence data in the historical data set is used as environment state parameters to be input into the deep reinforcement learning algorithm model, and an optimal day-ahead scheduling strategy is solved through an iterative training network model; and calculating the water consumption rate by using the obtained optimal scheduling strategy result, and evaluating the result. By introducing an Actor-Critic algorithm in deep reinforcement learning, an optimization decision strategy in a complex environment is realized, the economic operation efficiency and the power generation benefit of the system are effectively improved, and the water consumption rate of cascade hydropower is remarkably reduced.

The invention relates to the technical field of energy systems, and discloses an energy system intelligent decision-making method, which comprises the following steps: acquiring four-flow data of an industrial energy system, and constructing a four-flow parameter model based on a production flow topological graph; constructing a multi-agent reinforcement learning environment, taking the four-flow parameter model as a state space of the multi-agent reinforcement learning environment, and determining an action space and a reward function of each agent; performing domain adjustment on the large language model based on the industrial energy system knowledge base; through combination of a multi-agent reinforcement learning model and a large language model, a decision strategy is dynamically adjusted according to real-time data feedback, and decision feasibility verification is performed based on a physical mechanism model. According to the method, the four-flow integrated parameter model is constructed, and a multi-agent reinforcement learning and large language model cooperation mechanism is introduced, so that the problems of data splitting and the like in a traditional energy management system are effectively solved, and the intelligent level of energy system decision making is improved.

The invention discloses a laser operation process control system based on multi-element fusion, and particularly relates to the field of automatic control, which comprises a multi-element perception fusion module, an intelligent decision module, a multi-actuator cooperative control module, a system general control and man-machine interaction module and a self-learning and optimization module, geometric morphology, a temperature field, plasma features and environmental data of an operation area are collected in real time through integration of multiple sensors, a fusion situation information graph is generated through feature extraction and fusion, forward prediction is carried out in combination with a high-fidelity digital twin model, morphology evolution, thermodynamic behaviors and potential defects of a machining area are predicted, and the machining precision is improved. Based on the prediction result, a laser, a movement mechanism and an auxiliary gas unit are synchronously controlled through a high-speed industrial network, collaborative operation, coordination task scheduling, state monitoring and man-machine interaction of multiple actuators are achieved, and model parameters and decision strategies are continuously optimized by comparing actual data with the prediction result.

The invention provides a multi-agent collaborative decision-making system and method for intelligent manufacturing, and relates to the technical field of multi-agent collaboration. Multi-source heterogeneous sensing data are collected and processed, and various sensing feature data are extracted; constructing an edge computing node, deploying a convolutional neural network model at the edge computing node, processing various sensing feature data, and judging abnormal conditions of various sensing data; constructing a digital twinborn model, and when an abnormal condition of an edge computing node is received, integrating various sensing feature data by the digital twinborn model, generating a collaborative decision strategy, and performing collaborative control on multiple groups of agents; and task allocation is performed on a plurality of intelligent monomers in each group of intelligent agents based on an operation cost function, a capability balance condition and a priority, so that real-time perception and dynamic optimization of the manufacturing process are realized.

The invention discloses an SAR imaging satellite task planning method based on deep reinforcement learning, and the method comprises the steps: firstly analyzing the observation characteristics of SEOSs, building a mathematical model of the SEOSSP, and building a corresponding mathematical model through deep analysis of the SEOSSP, and employing the DRL to provide a solution; based on the model, a problem solving process is modeled as a Markov decision process, and two basic strategy models BPMs are applied to learn an effective decision strategy, so that a high-quality solution can be quickly generated, and the application prospect of the method in future SEOS scheduling optimization is highlighted.